Learn how to use Cloudera Director, Microsoft Active Directory, and Centrify Express to deploy a secure EDH cluster for workloads in the public cloud.
There are several best practices for deploying a secure Apache Hadoop-powered enterprise data hub (EDH) cluster on Amazon Web Services (AWS), including use of Centrify Express for Linux-to-Active Directory host integration and Microsoft Active Directory as the core integration point for identity, authentication, authorization, and public key infrastructure (PKI).
Using Apache Impala (incubating) on top of Apache Kudu (incubating) has significant performance benefits
Apache Kudu (incubating) is the newest addition to the set of storage engines that integrate with the Apache Hadoop ecosystem. The promise of Kudu is to deliver high-scan performance, targeting analytical workloads, while allowing users to concurrently insert, update, and delete records. With these properties, Kudu becomes a viable alternative to existing combinations of HDFS and/or Apache HBase to achieve similar results with less complicated ETL pipelines,
Thanks to Jonathan Natkins, a field engineer from StreamSets, for the guest post below about using StreamSets Data Collector—open source, GUI-driven ingest technology for developing and operating data pipelines with a minimum of code—and Cloudera Search and HUE to build a real-time search environment.
As pressure mounts on data engineers to deliver more data from more sources in less time, StreamSets Data Collector can serve as a linchpin in the data management process,
Recently, GoDataDriven installed a Cloudera Enterprise (CDH + Cloudera Manager) cluster on Microsoft Azure. This two-part series, written by Alexander Bij and Tünde Alkemade and republished with permission, includes information about use case, design, and installation.
Processing large amounts of unstructured data requires serious computing power and also maintenance effort. As load on computing power typically fluctuates due to time and seasonal influences and/or processes running on certain times,
Creating and training machine-learning models is more complex on distributed systems, but there are lots of frameworks for abstracting that complexity.
There are more options now than ever from proven open source projects for doing distributed analytics, with Python and R become increasingly popular. In this post, you’ll learn the options for setting up a simple read-eval-print (REPL) environment with Python and R within the Cloudera QuickStart VM using APIs for two of the most popular cluster computing frameworks: Apache Spark (with MLlib) and H2O (from the company with the same name).